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Junxiang Peng

PhD Student

Junxiang Peng


Project title

Managing and Optimizing Irrigation and Fertilization by Satellite and Small Unmanned Air Vehicle


Potato is one of the most important crops in Europe, including Denmark. Wheat is the dominant crop in temperate countries being used for human food and livestock feed (Shewry 2009). In Denmark, wheat (winter and spring wheat) is the dominant crop too. All of them need the precision management.

Currently, much less attention have been paid to the spatio-temporal co-scheduling of irrigation and N fertilization, i.e., precision agriculture, to minimize losses to the ground and surface waters. As irrigation allows fertilizers to be solubilized and taken up by crops immediately after application, co-scheduling opens up a time-window for assessing soil N mineralization and adjusting split N applications both spatially and temporally.

This PhD project focuses on the use of data from satellite, drone and rapidscan to “correlate” reflectance information of potato and wheat with their physiologic data (leaf area index (LAI), leaf chlorophyll content (LCh), leaf nitrogen content (LN)) and soil data (soil water content (SWC), soil nitrogen content (SN)). If a correlation can be established by means of statistic methods, then it can be used to manage irrigation and fertilization.  Moreover, we can compare the calculated irrigation and fertilization time and amounts with those commonly applied by the farmers and suggest improvements for the better agricultural operation. Finally, with accounted spatio-temporal variability, the results are expected to support agro-environmental and economic benefits by reducing irrigation and fertilization amounts.

The PhD project is part of a larger EU project Potential and the MOIST project financed by Innovation Fund Denmark.

Objective and methods

1. To collect three-seasons (2017-2019) field measurements of potato, and one or two season (2018-2019) of wheat, for development, growth, and soil water and N status, under potential and stressed conditions.

2. To collect remotely sensed data from Sentinel-2 and small unmanned aerial vehicle (SUAV) and to subsequently calibrate and validate these data using the field-collected data. 

3. To utilize the validated remote sensing platform for monitoring and managing drought stress and suboptimal fertilization conditions for potato and wheat fields in Denmark.


Five main hypotheses will be tested:

1. Deficit irrigation increases potato transpiration and positively interacts with split-N fertilization, resulting in higher resource use efficiency and increased yields.

2. Drought stress and transpiration decrease can be mapped with high spatial resolution and high correlation to drone thermal imagery.

3. Visible, red, infrared and thermal bands have robust capability for N and water stress monitoring during in-season growth of potato and wheat.

4. The relationship between reflectance indices (NDVI, RVI, red edge) and crop truth data (e.g. leaf water content, leaf nitrogen content, leaf area index) is powerful enough to explain variations in crop growth under drought and nitrogen stress conditions, and to guide irrigation and fertilization regimes.

5. Software assimilating remotely sensing information can optimize the spatial and temporal fertilization and irrigation.






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